Level 1B Data Research Articles

USING OPTICAL SATELLITE IMAGES AND SATELLITE ALTIMETRY DATA TO ESTIMATE VOLUME VARIATIONS IN DAMS

Abstract. This study focused on monitoring the water volume variations of the Doroudzan dam reservoir in Shiraz, Iran, using satellite observations. In particular, Sentinel-3 altimetry mission (SRAL) Level-1B and Level-2 data were employed to calculate water level changes, addressing the limitations in accuracy for inland and shallow waters. Re-tracking of returned waveforms was applied to improve the accuracy of Level-2 altimetry results. Additionally, Sentinel-2 optical images were utilized to monitor the water surface area of the dam reservoir. The results demonstrated that re-tracking the returned waveforms significantly improved the water level observations compared to Level-2 data. The analysis extended to comparing the time series of water surface area estimated from Sentinel-2 images with in-situ data, revealing a high accuracy of 5.39%. Combining optimum water level and surface area data in Heron's equation facilitated the calculation of water volume variations. A remarkable correlation of 95.27% was found when comparing the time series of estimated water volume variations and in-situ data. This study underscores the effectiveness of Copernicus satellites, particularly Sentinel-3 and Sentinel-2 missions, in monitoring inland water bodies and demonstrates the reliability of the techniques employed for tracking dam reservoir volume variations.

Cloud top heights and aerosol layer properties from EarthCARE lidar observations: the A-CTH and A-ALD products

Abstract. The high-spectral-resolution Atmospheric Lidar (ATLID) on the Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) provides vertically resolved information on aerosols and clouds with unprecedented accuracy. Together with the Cloud Profiling Radar (CPR), the Multi-Spectral Imager (MSI), and the Broad-Band Radiometer (BBR) on the same platform, it allows for a new synergistic view on atmospheric processes related to the interaction of aerosols, clouds, precipitation, and radiation at the global scale. This paper describes the algorithms for the determination of cloud top height and aerosol layer information from ATLID Level 1b (L1b) and Level 2a (L2a) input data. The ATLID L2a Cloud Top Height (A-CTH) and Aerosol Layer Descriptor (A-ALD) products are developed to ensure the provision of atmospheric layer products in continuation of the heritage from the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). Moreover, the products serve as input for synergistic algorithms that make use of data from ATLID and MSI. Therefore, the products are provided on the EarthCARE joint standard grid (JSG). A wavelet covariance transform (WCT) method with flexible thresholds is applied to determine layer boundaries from the ATLID Mie co-polar signal. Strong features detected with a horizontal resolution of 1 JSG pixel (approximately 1 km) or 11 JSG pixels are classified as thick or thin clouds, respectively. The top height of the uppermost cloud layer together with information on cloud layering are stored in the A-CTH product for further use in the generation of the ATLID-MSI Cloud Top Height (AM-CTH) synergy product. Aerosol layers are detected as weaker features at a resolution of 11 JSG pixels. Layer-mean optical properties are calculated from the ATLID L2a Extinction, Backscatter and Depolarization (A-EBD) product and stored in the A-ALD product, which also contains the aerosol optical thickness (AOT) of each layer, the stratospheric AOT, and the AOT of the entire atmospheric column. The latter parameter is used to produce the synergistic ATLID-MSI Aerosol Column Descriptor (AM-ACD) later in the processing chain. Several quality criteria are applied in the generation of A-CTH and A-ALD, and respective information is stored in the products. The functionality and performance of the algorithms are demonstrated by applying them to common EarthCARE test scenes. Conclusions are drawn for the application to real-world data and the validation of the products after the launch of EarthCARE.

Sentinel-6 MF Poseidon-4 radar altimeter: Main scientific results from S6PP LRM and UF-SAR chains in the first year of the mission

Poseidon-4 is a dual-frequency redundant radar altimeter on board the European Commission Copernicus Programme Sentinel-6 Michael Freilich satellite, that represents a significant breakthrough with respect to its predecessors Jason-class altimeters due to its digital architecture and to its innovative measurements and calibration modes.In the framework of the Sentinel-6 Michael Freilich commissioning preparatory activities, CNES has contracted CLS for the development of a Sentinel-6 Processing Prototype (S6PP) application. S6PP is a multi-chain processing suite able to process Sentinel-6 Level-1A and Level-1B data products up to Level-2. The novel algorithms developed in the CNES/CLS research and development activities are implemented within S6PP and validated to support the different thematic applications (in particular inland water and ocean) and in view of promoting them for possible implementation in the operational ground segment.The present work covers in particular the main results over open ocean for the main altimetric geophysical variables over the sea surface (sea surface height anomaly, significant wave-height, sigma-nought and wind speed) derived by the Low-Resolution Mode (LRM) and High-Resolution Mode (HRM) chains of S6PP in terms of precision, accuracy, spectral content and measurement stability.Given the reported variation of the payload in-orbit temperatures along with the reported instrumental ageing, and given the tight requirement to measure the GMSL (Global Mean Sea Level) in seamless continuity with Jason-3, the clear goal for S6PP was to process the S6-MF data with the minimum possible level of approximations along the processing pipeline but still maintaining a very efficient prototype from the computational point of view.For this scope, a novel and computationally efficient numerical retracking scheme with interface to the in-flight PTR (Point Target Response) provided by the instrument calibration chain has been put in place within S6PP for both the Low-Resolution and High-Resolution modes whereas the Delay-Doppler beam-forming is carried out by applying the range walk correction based on a computationally efficient algorithm (Chirp Zeta-Transform).The impact of the range walk correction and of the in-flight PTR interface is assessed for HRM and LRM, respectively.The paper shows that the proposed processing baseline ensures a dataset robust from the currently known instrumental degradation or ageing issues, both in LRM and HRM mode and, once this is done, that Sentinel-6 Michael Freilich global mean sea level measurement is in line with the one measured by Jason-3.

Reprocessed precise science orbits and gravity field recovery for the entire GOCE mission

ESA’s Gravity field and steady-state Ocean Circulation Explorer (GOCE) orbited the Earth between 2009 and 2013 for the determination of the static part of Earth’s gravity field. The GPS-derived precise science orbits (PSOs) were operationally generated by the Astronomical Institute of the University of Bern (AIUB). Due to a significantly improved understanding of remaining artifacts after the end of the GOCE mission (especially in the GOCE gradiometry data), ESA initiated a reprocessing of the entire GOCE Level 1b data in 2018. In this framework, AIUB was commissioned to recompute the GOCE reduced-dynamic and kinematic PSOs. In this paper, we report on the employed precise orbit determination methods, with a focus on measures undertaken to mitigate ionosphere-induced artifacts in the kinematic orbits and thereof derived gravity field models. With respect to the PSOs computed during the operational phase of GOCE, the reprocessed PSOs show in average a 8–9% better consistency with GPS data, 31% smaller 3-dimensional reduced-dynamic orbit overlaps, an 8% better 3-dimensional consistency between reduced-dynamic and kinematic orbits, and a 3–7% reduction of satellite laser ranging residuals. In the second part of the paper, we present results from GPS-based gravity field determinations that highlight the strong benefit of the GOCE reprocessed kinematic PSOs. Due to the applied data weighting strategy, a substantially improved quality of gravity field coefficients between degree 10 and 40 is achieved, corresponding to a remarkable reduction of ionosphere-induced artifacts along the geomagnetic equator. For a static gravity field solution covering the entire mission period, geoid height differences with respect to a superior inter-satellite ranging solution are markedly reduced (43% in terms of global RMS, compared to previous GOCE GPS-based gravity fields). Furthermore, we demonstrate that the reprocessed GOCE PSOs allow to recover long-wavelength time-variable gravity field signals (up to degree 10), comparable to information derived from GPS data of dedicated satellite missions. To this end, it is essential to take into account the GOCE common-mode accelerometer data in the gravity field recovery.

On-Orbit Absolute Radiometric Calibration and Uncertainty Analysis of OCM2 Sensor onboard Oceansat-2 Satellite

ABSTRACT This study describes On-Orbit absolute radiometric calibration for the Ocean Colour Monitor2 (OCM2) onboard Oceansat-2 satellite through a vicarious calibration experiment performed at the Great Rann of Kutch calibration site in Gujarat, India, in February 2022. To achieve accurate and consistent calibration for the OCM2 channels, a reflectance-based calibration method was used which relies on synchronous in-situ measurements of surface reflectance and atmospheric parameters at the time of satellite overpass. In this exercise, the 6 SV (Second Simulation of a Satellite Signal in the Solar Spectrum Vector) radiative transfer (RT) model was used to simulate the Top Of Atmosphere (TOA) spectral radiance for the OCM2 channels. The on-orbit radiometric performance/changes were derived by comparing the 6 SV simulated TOA radiance with those of the OCM2 Level 1B (L1B) data product. The results indicate that the average gain for band 1 to band 8 was found in the range from 0.88 to 1.23 and the relative error from 1.58% to 18.79% for the OCM2 sensor. However, the Root-Mean-Square-Error (RMSE) between the OCM2 measured and the 6 SV simulated TOA radiance data range from 0.17 (µW cm−2 sr−1 nm−1) at 443 nm to 2.04 (µW cm−2 sr−1 nm−1) at 620 nm. Furthermore, we analysed in detail the various uncertainties in this approach emanating from surface reflectance, atmospheric conditions (ozone, water vapour and aerosol optical depth), aerosol-type assumption in the RT model, BRDF, and inherent accuracy of the 6 SV RT model. The overall uncertainty was within 6% estimated using the reflectance-based calibration method.

Analytical Study of the Changes in Brightness Temperature Based on the Tectonic Field Associated With Three Earthquakes in the Eastern Tibetan Plateau

The thermal infrared brightness temperature (BT) of the eastern Tibetan Plateau (TP) was retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) level-1B data. The multiyear averaged BT background field was subtracted from the punctual BT data to yield monthly BT spatial anomaly, and calculated time series of BT for the secondary blocks. Then, the spatial and temporal changes in the BT of the study area before the Menyuan M6.4, Zaduo M6.2, and Jiuzhaigou M7.0 earthquakes were investigated and analyzed based on the tectonic setting. The results show the following. The spatial BT radiation enhancement frequency rose remarkably before strong earthquakes; each of the three earthquakes was preceded by marked spatiotemporal continuous BT anomalies. The tectonic setting significantly influences the BT anomaly feature. The spatial BT anomaly was not notable in the Qaidam and Qilian block before the Menyuan earthquake; the spatial BT anomaly mainly appeared in the Qiangtang and Bayan Har blocks before the Zaduo and Jiuzhaigou earthquakes. The Qiangtang and Bayan Har block’s BT time series curves have similar features. The Qaidam and Qilian block’s BT time series curves have analogous shapes. The three earthquakes may be regarded as one seismic event induced by a stage of tectonic stress enhancement rather than three independent occasions. The spatial BT anomalous behavior before earthquakes is, to a great extent, like the result of the rock stress loading experiment; the rock compression and the lithosphere-atmosphere-ionosphere coupling (LAIC) may be the main reasons for the intensification of the BT radiation.

GEDI waveform metrics in vegetation mapping—a case study from a heterogeneous tropical forest landscape

The distribution of different vegetation types is important information for landscape management, especially in the context of tackling global environmental change. Vegetation types can be mapped using satellite and airborne passive remote sensing. However, spectrally similar yet structurally different vegetation types, like different tree-dominated land covers, are often challenging to map using spectral information alone. We examined the potential of vertical vegetation structure acquired in the global ecosystem dynamics investigation (GEDI) mission that harnesses a space-borne waveform lidar sensor in vegetation mapping across a heterogeneous tropical landscape in Cambodia. We extracted 121 waveform metrics from Level-1B and Level-2A data products at 1062 locations across five key vegetation types. After reducing the relative height variables’ dimensionality through simple linear regressions, we developed a Random Forest classifier to predict vegetation classes based on 23 GEDI metrics. We then used this model to classify the vegetation types across more than 77 000 GEDI footprints in the study area. GEDI metrics alone were useful in identifying vegetation types with 81% accuracy. Cropland/grassland class had the highest prediction accuracy (user’s accuracy [UA] = 89%; producer’s accuracy [PA] = 91%), while dry deciduous forest had the lowest accuracy (UA = 73%; PA = 69%). By comparing the GEDI-only classification with an optical-radar map, we found that structural and topographic information from GEDI Level-1B and Level-2A can complement the spectral information in assessing natural habitats that neighbor other vegetation types in a heterogeneous landscape. The highest classification accuracy at the footprint scale was obtained from the combination of GEDI, Sentinel-1, and Sentinel-2 (88.3%). We also demonstrated how wall-to-wall vegetation mapping is possible by combining the three data sources. These findings expand the potential use of GEDI waveform lidar data in supporting the development of policy-relevant maps that depict the distribution of forests together with other vegetation types.

Petrological and Mineralogical Characteristics of Exposed Materials on the Floors of the Lavoisier and Surrounding Craters

Five floor-fractured craters (FFCs), Lavoisier crater and four craters surrounding it (Lavoisier C, Lavoisier E, Lavoisier F, and Lavoisier H), are distributed along the boundary between the northwestern part of the Oceanus Procellarum and the highlands. This study examines the uplifted or exposed materials on the fractured floors of these five impact craters using petrological, mineralogical, and morphological analyses. We inferred the processes that uplifted or exposed the materials from the subsurface to the crater floor using the Chandrayaan-1 Moon Mineralogy Mapper (M3) level 1b (thermally and topographically corrected spectral radiance) data and level 2 (spectral reflectance) data. The elemental abundances, petrological, and mineralogical characteristics of the study regions were mapped. We confirm that mare basalts and dark mantle deposits exist on the floors of these five craters. These two materials (mare basalts and dark mantle deposits) were used to identify minerals exposed on the floor surface of craters using spectral reflectance spectra. Two mineral groups were identified: pigeonite (or orthopyroxene in norite (low-Ca pyroxene) occurred in the craters Lavoisier, Lavoisier F, and Lavoisier H, and subcalcic augite (high-Ca pyroxene) occurs in the craters Lavoisier C and E. Our approach demonstrates that the characterization of uplifted or exposed surface minerals using elemental maps, spectral parameter composite maps, and reflectance spectra can provide information critical for prospective studies involving lunar geology and in situ resource utilization.

Assessment of the TsHARP method for spatial downscaling of land surface temperature over urban regions

Nowadays, acquiring high spatial and temporal land surface temperature (LST) is one of the significant factors in urban climate studies. This study aims to assess the sharpening thermal imagery (TsHARP) method, which is developed for determining the LST in urban regions. Whereas the original TsHARP method correlates the Normalized Difference Vegetation Index (NDVI) with the LST, the current study seeks to evaluate the performance of the TsHARP method by correlating between the LST and NDVI over urban areas. Additionally, it is intended to investigate the relationship between the impervious surfaces (IS) and LST. At this point, the Advanced Spaceborne Thermal Emission and Reflection Radiometer ASTER image of the year 2003 was used to retrieve the IS as well as the LST of the Kuala Lumpur city, Malaysia. To retrieve the main LST, IS and NDVI, ASTER Level1B data were used. On the other hand, ASTER land surface temperature products (AST08) was utilized to evaluate the downscaled LST (DLST) image. With selection of the ~1/4 of the pixels with lowest coefficient of variation (CV), the LST has more correlation with the IS in comparison with the NDVI, while the correlation between the IS and LST was increased in this study. The IS index illustrates the most optimal correlation for the urban LST (R2 = 0.59). Furthermore, the TsHARPIS-based method depicted a good correlation between the DLST and the ASTER LST product (RMSE = 1.5 °C, R2 = 0.83) compared to the NDVI (RMSE = 4 °C, R2 = 0.73). Consequently, it is found that the application of TsHARPIS-based method to ASTER remote sensing data is a robust/low-cost approach for spatial downscaling of the LST over urban regions.

Ozone Monitoring Instrument (OMI) collection 4: establishing a 17-year-long series of detrended level-1b data

Abstract. The Ozone Monitoring Instrument (OMI) was launched on 15 July 2004, with an expected mission lifetime of 5 years. After more than 17 years in orbit the instrument is still functioning satisfactorily and in principle can continue doing so until the expected decommissioning of its platform Aura in 2025. In order to continue the datasets acquired by OMI and the Microwave Limb Sounder, the mission was extended up to at least 2023. Actions have been taken to ensure the proper functioning of the OMI operations, the data processing, and the calibration monitoring system until the eventual end of the mission. For the data processing a new level-0 (L0) to level-1b (L1b) data processor was built based on the recent developments for the TROPOspheric Monitoring Instrument (TROPOMI). With corrections for the degradation of the instrument now included, it is feasible to generate a new data collection to supersede the current collection-3 data products and reprocess the data of the entire mission up to now. This paper describes the differences between the collection-3 and collection-4 data. It will be shown that the collection-4 L1b data comprise a clear improvement with respect to the previous collections. By correcting for the gentle optical and electronic aging that has occurred over the past 17 years, OMI's ability to make trend-quality ozone measurements has further improved.

Estimate of daytime single-layer cloud base height from advanced baseline imager measurements

Cloud base height (CBH) is an important parameter to describe cloud state and is highly related to the vertical motions in the atmosphere. CBH information is critical for both aviation safety and synoptic analysis. In this study, daytime CBH is estimated directly from Geostationary Operational Environmental Satellite-R Series (GOES-16) Advanced Baseline Imager (ABI) level 1b data and the European Centre for Medium-Range Weather Forecasts' (ECMWF) fifth generation reanalysis (ERA5) data using the Gradient Boosted Regression Trees (GBRT) machine learning technique. The CBH estimate algorithm, which is named as GETCBH, covers the same areal extent as the full disk of the ABI/GOES-16 and only for single-layer clouds. The 2-years of CBH measurements from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite is used as the label (which is the true value/class of the model output for regression/classification problem in machine learning terminology). A quality flag algorithm using another machine learning technique, the Gradient Boosted Decision Trees machine learning technique is developed to provide a confidence level for the CBH estimate. The evaluations show an overall root mean square error (RMSE) of 1.87 km and Pearson's correlation coefficient (Pearson's r) of 0.92 before any quality control. After excluding CBH estimates with low confidence (19.2% of all samples), the RMSE is reduced to 1.14 km, Pearson's r increases to 0.97, and 96% of the estimates are within 2 km of the CALIOP results. By analyzing model bias and feature importance, cloud phase information has the biggest impact on the CBH estimate, although all input features have positive impact on the estimate accuracy. Limited by the penetrability of CALIOP, GETCBH is valid for clouds with COD < 8.5. The CBH estimates have reduced accuracy (Pearson's r of 0.88) for optically thin clouds (clouds with cloud optical depth [COD] < 0.1) where little cloud information is contained in the ABI measurements, as well as for optically thick clouds (clouds with COD ≥ 3) where a larger proportion of opaque clouds is excluded. Furthermore, for the GBTCBH model using 9 months of CloudSat measurements as label, the CBH estimates are improved with an RMSE of 1.41 km and Pearson's r of 0.92. In a case study of Hurricane Dorian, CBHs for most of the single-layer clouds are successfully estimated with small errors and flagged with high confidence, for both high and low clouds. Deep convective clouds and multi-layer clouds, both of which are not included in the training, are reasonably flagged as low confidence with large CBH estimate errors. In this particular case, 65% of cloudy pixels have CBH estimate with high confidence in the scene. Daytime CBH with high spatial (2 km) and temporal (10 min) resolution can be derived from ABI measurements using this methodology.

Radiometric Calibration Scheme for COCTS/HY-1C Based on Image Simulation From the Standard Remote-Sensing Reflectance

The data quality of the satellite-retrieved water-leaving reflectance (Rrs) depends on the accuracy of radiometric calibration and the performance of atmospheric correction. A radiometric calibration scheme (RCS) has been developed to ensure the accuracy of Rrs through the gain adjustment factors (GAFs) to adjust the satellite calibrated data. The GAF is obtained from the ratio of the simulated reflectance at the top of atmosphere to the calibrated values. The simulated reflectance is computed by a satellite image simulation model (SISM) based on a dataset of climatological global Rrs images according to the same geometric angles of the image pixels. The dataset, taken as a kind of the pseudo-invariant calibration sites for in situ measurements, is generated from the average of standard satellite-retrieved Rrs during more than two decades (1997-2019). The SISM inputs the aerosol properties retrieved from the satellite level 1B data (L1B) and uses the same algorithms of the data-processing system. The results show that the accuracy of the calibration of the website downloaded Chinese Ocean Color and Temperature Scanner on the Haiyang-1C satellite (COCTS/HY-1C) is beyond the requirement of the operational data-processing system (higher than 10%). The daily GAFs can be used to recalibrate the L1B data and monitor the daily sensor degradations. The influences of GAFs are assessed on different meteorological conditions, indicating that the values decrease with the increase of the aerosol optical depths (AODs) but the average of the GAF image is little affected by the meteorological conditions. The uncertainty of GAFs was tested by the different inputs of Rrs values and the results show that they are actually little affected by errors of the Rrs inputs. Therefore, the RCS, taking the advantage of vicarious calibration, offers a tool to recalibrate the COCTS/HY-1C L1B data for the data reprocessing system.

Radio Frequency Interference Detection for SMAP Radiometer Using Convolutional Neural Networks

Passive remote sensing is a crucial technology for climate studies and Earth science. NASA's soil moisture active passive (SMAP) is a remote sensing observatory that uses passive microwave radiometer measurements to estimate soil moisture and detect the freeze or thaw state. Despite operating in the protected band of the radio spectrum (1400-1427 MHz), the radiometer's measurements are nonetheless tainted by radio frequency interference (RFI). An increasing number of radio-frequency transmissions such as those from air surveillance radars, 5G wireless communications, and unmanned aerial vehicles are contributing to RFI through either out-of-band emissions or operating in-band illegally. Physical modeling to detect RFI globally might prove to be challenging as RFI can be generated from single as well as multiple sources and these can be divided as pulsed or continuous wave RFI. In this study, a deep learning (DL) based RFI detection method is proposed with a novel convolutional neural network framework that can detect different types of RFI on a global scale. This is a data-driven approach where the detection framework learns directly from the SMAP data products to make a decision whether a certain footprint is RFI contaminated or not. SMAP's level 1A data products containing antenna counts of different raw moments along with Stokes parameters are used in this study to produce spectrograms and level 1B data products containing the quality flags are used to dynamically label those spectrograms. This study's robust DL framework provided the highest accuracy with the raw moments of horizontal polarization (99.99%) to detect RFI globally.

Effects of New Level-1B Data on GRACE Temporal Gravity Field Models and Precise Orbit Determination Solutions

The quality of Gravity Recovery and Climate Experiment (GRACE) observation is the prerequisite for obtaining the high-precision GRACE temporal gravity field model. To study the influence of new-generation GRACE Level-1B Release 03 (RL03) data and the new atmosphere and ocean de-aliasing (AOD1B) products on recovering temporal gravity field models and precise orbit determination (POD) solutions, we combined the global positioning system and K-band ranging-rate (KBRR) observations of GRACE satellites to estimate the effect of different data types on these solutions. The POD and monthly gravity field solutions are obtained from 2005 to 2010 by SHORDE software developed by the Shanghai Astronomical Observatory. The post-fit residuals of the KBRR data were decreased by approximately 10%, the precision of three-direction positions of the GRACE POD was improved by approximately 5%, and the signal-to-noise ratio of the monthly gravity field model was enhanced. The improvements in the new release of monthly gravity field model and POD solutions can be attributed to the enhanced Level-1B KBRR data and the AOD1B model. These improvements were primarily due to the enhanced of KBRR data; the effect of the AOD1B model was not significant. The results also showed that KBRR data slightly improve the satellite orbit precision, and obviously enhance the precision of the gravity field model.

Total ozone measurements using IKFS-2 spectrometer aboard Meteor-M N2 satellite in 2019–2020

ABSTRACT Ozone is one of the key atmospheric trace gases that protects life on Earth from harmful solar ultraviolet radiation. The importance of ozone and its influence on various atmospheric processes led to the creation of a global ozone monitoring system. Satellite remote sensing methods make a significant contribution to the analysis of spatial distribution and temporal evolution of ozone content and its anomalies. We have presented a global (60° S – 90° N) total ozone column (TOC) data product derived using the IKFS-2 (Infrared Fourier Spectrometer) instrument aboard the Meteor-M N2 satellite in 2019–2020. The IKFS-2 measures outgoing thermal radiation in 5–15 µm (660–2000 cm−1) spectral range with an un-apodized spectral resolution of 0.4 cm−1. The retrieval technique is based on the artificial neural network (ANN) algorithm and the method of principal components. For the ANN training, we used the level 2 TOC measurements by the OMI instrument aboard the Aura satellite, thus solving the problem of calibrating the IKFS-2 TOC data product. The uncertainty estimated for IKFS-2 TOC measurements equals 10 DU (~3%). We compared IKFS-2 TOC data with ground-based measurements at 14 observational sites in the Northern Hemisphere equipped with the Dobson and Brewer spectrometers. For spatial mismatch of 70 km and temporal mismatch of 1 h, we observed the mean bias between IKFS-2 and ground-based (direct solar measurements) TOC datasets of −1.46% with a standard deviation of 2.57%. We demonstrated that global distribution fields of IKFS-2 TOCs are consistent with OMI measurements and ECMWF ERA5 reanalysis data. In general, for seasonal means, IKFS-2 underestimates OMI TOCs by 0.1–1.0%, and ERA5 TOCs by 1.8–3.1%, depending on the season. We analysed IKFS-2 TOC retrievals in 2018/2019 versus 2019/2020 Arctic winters. Differences in TOCs resulting from differences in stratospheric dynamic for two winter-spring seasons are well captured by IKFS-2. In 2020 relative to 2019, the averaged (50–90° N) deficit in TOCs comprises 72 DU (17%), 68 DU (16%), 84 DU (20%), and 70 DU (17%) for January, February, March, and April, respectively. Dataset presented is the first data product of continuous TOC measurements derived using publicly available IKFS-2 level 1b data. Further, we plan to obtain, analyze, and validate IKFS-2 TOCs for an extended period of measurements (2015–2020).

S-NPP VIIRS Thermal Emissive Bands 10-Year On-Orbit Calibration and Performance

The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership Program (S-NPP) satellite, launched in late 2011, has reached the decade landmark under successful operations. VIIRS has 22 spectral bands, 7 of which are thermal emissive bands (TEB) that cover the 3.70 to 11.84 μm wavelength range. Over the years, VIIRS TEB observations have been used to generate several data products (e.g., surface/cloud/atmospheric temperatures, cloud top altitude, and water vapor properties). The VIIRS TEB calibration uses a quadratic algorithm and is referenced to an on-board blackbody with temperature measurements traceable to the National Institute of Standards and Technology standard. This manuscript provides an overview of the VIIRS instrument operations and TEB calibration activities and algorithms used in the level 1B data and describes the TEB on-orbit performance for S-NPP VIIRS. The 10-year on-orbit performance of the S-NPP VIIRS TEB has generally been stable, and the degradations in the S-NPP TEB detector responses are minor after a decade in orbit. The noise characterization performance repeatedly meets the design requirements for all TEB detectors as well. On-orbit changes in the TEB response-versus-scan-angle, based on pitch maneuver observations, have been demonstrated to be extremely small. Moreover, multiple time series over select ground targets have shown that the sensor’s on-orbit performance is quite stable.

Reconstruction Method of Satellite Gravity Gradient Measurement Angular Velocity by Combining Star Tracker Quaternion

Objectives In order to calculate the high precision gravity gradient in level 1b (L1b) data processing of gravity field and steady-state ocean circulation explorer(GOCE)satellite, it is necessary to accurately measure the satellite attitude angular velocity. In the coordinate system transformation of the attitude data of astrology, the low precision angular velocity components of a single astrology instrument will transmit the noise to other angular velocity components, which will affect the calculation accuracy of the whole gravity gradient. Methods In this paper, based on the noise characteristics of the astrological instrument, the weighted matrix of the noise distribution on each axis of the astrological instrument is constructed, and the optimal attitude quaternion is solved jointly for the attitude quaternion of two or more astrological instruments, so as to provide accurate attitude control for subsequent angular velocity recovery. Results Compared with the single astro-meter calculation method, the accuracy of the optimal attitude angular velocity components wy and wz obtained by the combination of multiple astro-meters can reach about 1 order of magnitude higher within 10-100 mHz, and the noise level is up to 10-6 rad/s, which can effectively suppress the noise propagation caused by the low precision angular velocity components in coordinate system transformation. Conclusions The results show that the satellite attitude angular velocity calculated by the combination of multiple astrological instruments can be significantly improved in the whole power spectral density, the power spectral density of gravity gradient trace values calculated by the combined method of multiple astrologers is also significantly improved compared with the single astrologer method.

INVERSING GLACIER ALBEDO IN HIGH MOUNTAIN ASIA BASED ON SENTINEL-3 SATELLITE DATA

Abstract. Surface albedo, defined as the ratio of the upward to downward solar irradiance, is an important climate parameter in the surface energy budget. Among them, glacier albedo is the link between glaciers and the hydration process of climate and cold zones, and it is also an important factor restricting the development of distributed glacier energy-material balance model. Changes in the albedo of ice and snow can alter the energy balance of the entire geo-gas system and can also cause local and even global climate change. Glaciers in High Mountain Asia have experienced heterogeneous rates of loss since the 1970s. The positive feedback effect of ice and snow makes its albedo an important indicator of ice and snow surface mass balance. This study used partly Sentinel-3 level-1b data of High Mountain Asia in 2017, RGI (Randolph Glacier Inventory) 6.0 and SPP (Snow Properties Processor) developed by the ESA’s team to extract the region's mountain glacier albedo in SNAP (Sentinel Application Platform) environment. At present, the products of the whole year of 2017 have been produced, with a time resolution of one month and a spatial resolution of 300 m.

Electrical conductivity and temperature of the Earth's mantle inferred from Bayesian inversion of Swarm vector magnetic data

Study of induced magnetic field is a powerful way to sound Earth internal structure. This work presents a full analysis and interpretation in terms of electrical conductivity and temperature of vector magnetic field measurements from Swarm Level1b data product from 26/11/2013 to 31/12/2019. Time series of the Gauss coefficients associated with the induced and inducing magnetic field are obtained from the data after removal of the core and lithospheric fields models and data selection. A Bayesian inversion of the induced field Gauss coefficients is then performed to obtain a new estimate of Earth's 1D mantle electrical conductivity down to 2000 km depth. This profile is fully compatible with the profiles derived from satellite and ground magnetic observatories data but does not present in the lower mantle the increase predicted by laboratory-based conductivity profile associated to classical mantle composition and temperature profile. Using the most recent database to model the electrical conductivity of all mineral mantle phases, two different methods are used to interpret Swarm data in terms of temperature for a given composition and water content. The first one is based on an interpretation of the conductivity estimates in terms of temperature by classical numerical root search. The second one consists in inferring a temperature probability density function from a Bayesian inversion of the Gauss coefficients associated to the induced magnetic field. Our results show that the later provide more reliable estimates of mantle temperatures, in relation to more physically grounded prior values. This second method provides also tighter constraints on the electrical conductivity estimates of the lower mantle.

Mission-Long Recalibrated Science Quality Suomi NPP VIIRS Radiometric Dataset Using Advanced Algorithms for Time Series Studies

Suomi NPP has been successfully operating since its launch on 28 October 2011. As one of the major payloads, along with microwave and infrared sounders (Advanced Technology Microwave Sounder (ATMS), Cross-track Infrared Sounder (CrIS)), and ozone mapping/profiling (OMPS) instruments, the Visible Infrared Imaging Radiometer Suite (VIIRS) has performed for well beyond its mission design life. Its data have been used for a variety of applications for nearly 30 environmental data products, including global imagery twice daily with 375 and 750 m resolutions, clouds, aerosol, cryosphere, ocean color and sea-surface temperature, a number of land products (vegetation, land-cover, fire and others), and geophysical and social economic studies with nightlights. During the early days of VIIRS operational calibration and data production, there were inconsistencies in both algorithms and calibration inputs, for several reasons. While these inconsistencies have less impact on nowcasting and near real-time applications, they introduce challenges for time series analysis due to calibration artifacts. To address this issue, we developed a comprehensive algorithm, and recalibrated and reprocessed the Suomi NPP VIIRS radiometric data that have been produced since the launch. In the recalibration, we resolved inconsistencies in the processing algorithms, terrain correction, straylight correction, and anomalies in the thermal bands. To improve the stability of the reflective solar bands, we developed a Kalman filtering model to incorporate onboard solar, lunar, desert site, inter-satellite calibration, and a deep convective cloud calibration methodology. We further developed and implemented the Solar Diffuser Surface Roughness Rayleigh Scattering model to account for the sensor responsivity degradation in the near infrared bands. The recalibrated dataset was validated using vicarious sites and alternative methods, and compared with independent processing from other organizations. The recalibrated radiometric dataset (namely, the level 1b or sensor data records) also incorporates a bias correction for the reflective solar bands, which not only addresses known calibration biases, but also allows alternative calibrations to be applied if so desired. The recalibrated data have been proven to be of high quality, with much improved stability (better than 0.3%) and accuracy (by up to 2%). The recalibrated radiance data are now available from 2012 to 2020 for users and will eventually be archived on the NOAA CLASS database.